Overview

Dataset statistics

Number of variables23
Number of observations3677
Missing cells6710
Missing cells (%)7.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 MiB
Average record size in memory628.4 B

Variable types

Categorical10
Text3
Numeric10

Alerts

area is highly overall correlated with bathroom and 5 other fieldsHigh correlation
bathroom is highly overall correlated with area and 5 other fieldsHigh correlation
bedRoom is highly overall correlated with area and 5 other fieldsHigh correlation
built_up_area is highly overall correlated with area and 4 other fieldsHigh correlation
carpet_area is highly overall correlated with area and 5 other fieldsHigh correlation
facing is highly overall correlated with built_up_areaHigh correlation
price is highly overall correlated with area and 7 other fieldsHigh correlation
price_per_sqft is highly overall correlated with priceHigh correlation
property_type is highly overall correlated with bedRoom and 2 other fieldsHigh correlation
servant room is highly overall correlated with bathroom and 1 other fieldsHigh correlation
super_built_up_area is highly overall correlated with area and 7 other fieldsHigh correlation
agePossession is highly imbalanced (62.5%)Imbalance
store room is highly imbalanced (55.7%)Imbalance
facing has 1045 (28.4%) missing valuesMissing
super_built_up_area has 1802 (49.0%) missing valuesMissing
built_up_area has 1987 (54.0%) missing valuesMissing
carpet_area has 1805 (49.1%) missing valuesMissing
area is highly skewed (γ1 = 29.73095613)Skewed
built_up_area is highly skewed (γ1 = 40.70657243)Skewed
carpet_area is highly skewed (γ1 = 24.33323909)Skewed
floorNum has 129 (3.5%) zerosZeros
luxury_score has 462 (12.6%) zerosZeros

Reproduction

Analysis started2026-01-07 06:13:35.213426
Analysis finished2026-01-07 06:14:01.423727
Duration26.21 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

property_type
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size248.6 KiB
flat
2818 
house
859 

Length

Max length5
Median length4
Mean length4.2336144
Min length4

Characters and Unicode

Total characters15567
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowflat
2nd rowflat
3rd rowflat
4th rowflat
5th rowflat

Common Values

ValueCountFrequency (%)
flat2818
76.6%
house859
 
23.4%

Length

2026-01-07T11:44:01.617529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-07T11:44:01.773510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
flat2818
76.6%
house859
 
23.4%

Most occurring characters

ValueCountFrequency (%)
f2818
18.1%
l2818
18.1%
a2818
18.1%
t2818
18.1%
h859
 
5.5%
o859
 
5.5%
u859
 
5.5%
s859
 
5.5%
e859
 
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)15567
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
f2818
18.1%
l2818
18.1%
a2818
18.1%
t2818
18.1%
h859
 
5.5%
o859
 
5.5%
u859
 
5.5%
s859
 
5.5%
e859
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)15567
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
f2818
18.1%
l2818
18.1%
a2818
18.1%
t2818
18.1%
h859
 
5.5%
o859
 
5.5%
u859
 
5.5%
s859
 
5.5%
e859
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)15567
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
f2818
18.1%
l2818
18.1%
a2818
18.1%
t2818
18.1%
h859
 
5.5%
o859
 
5.5%
u859
 
5.5%
s859
 
5.5%
e859
 
5.5%

society
Text

Distinct676
Distinct (%)18.4%
Missing1
Missing (%)< 0.1%
Memory size293.9 KiB
2026-01-07T11:44:02.479017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length49
Median length39
Mean length16.869695
Min length1

Characters and Unicode

Total characters62013
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique308 ?
Unique (%)8.4%

Sample

1st rowsignature global park 4
2nd rowsmart world gems
3rd rowpyramid elite
4th rowbreez global hill view
5th rowbestech park view sanskruti
ValueCountFrequency (%)
independent491
 
5.1%
the350
 
3.6%
dlf220
 
2.3%
park209
 
2.2%
city166
 
1.7%
emaar155
 
1.6%
global153
 
1.6%
m3m152
 
1.6%
signature150
 
1.6%
heights134
 
1.4%
Other values (783)7497
77.5%
2026-01-07T11:44:03.558174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e6710
 
10.8%
6003
 
9.7%
a5861
 
9.5%
r4171
 
6.7%
n4163
 
6.7%
i3830
 
6.2%
t3719
 
6.0%
s3472
 
5.6%
l2943
 
4.7%
o2755
 
4.4%
Other values (31)18386
29.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)62013
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e6710
 
10.8%
6003
 
9.7%
a5861
 
9.5%
r4171
 
6.7%
n4163
 
6.7%
i3830
 
6.2%
t3719
 
6.0%
s3472
 
5.6%
l2943
 
4.7%
o2755
 
4.4%
Other values (31)18386
29.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)62013
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e6710
 
10.8%
6003
 
9.7%
a5861
 
9.5%
r4171
 
6.7%
n4163
 
6.7%
i3830
 
6.2%
t3719
 
6.0%
s3472
 
5.6%
l2943
 
4.7%
o2755
 
4.4%
Other values (31)18386
29.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)62013
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e6710
 
10.8%
6003
 
9.7%
a5861
 
9.5%
r4171
 
6.7%
n4163
 
6.7%
i3830
 
6.2%
t3719
 
6.0%
s3472
 
5.6%
l2943
 
4.7%
o2755
 
4.4%
Other values (31)18386
29.6%

sector
Text

Distinct113
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size266.9 KiB
2026-01-07T11:44:04.139734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length26
Median length9
Mean length9.3209138
Min length7

Characters and Unicode

Total characters34273
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowsector 36
2nd rowsector 89
3rd rowsector 86
4th rowsohna road
5th rowsector 92
ValueCountFrequency (%)
sector3452
46.8%
road178
 
2.4%
sohna166
 
2.2%
85108
 
1.5%
102107
 
1.4%
92100
 
1.4%
6993
 
1.3%
9089
 
1.2%
8187
 
1.2%
6587
 
1.2%
Other values (106)2915
39.5%
2026-01-07T11:44:04.923895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o3807
11.1%
3705
10.8%
s3697
10.8%
r3697
10.8%
e3542
10.3%
c3503
10.2%
t3463
10.1%
11076
 
3.1%
0804
 
2.3%
8780
 
2.3%
Other values (21)6199
18.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)34273
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o3807
11.1%
3705
10.8%
s3697
10.8%
r3697
10.8%
e3542
10.3%
c3503
10.2%
t3463
10.1%
11076
 
3.1%
0804
 
2.3%
8780
 
2.3%
Other values (21)6199
18.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)34273
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o3807
11.1%
3705
10.8%
s3697
10.8%
r3697
10.8%
e3542
10.3%
c3503
10.2%
t3463
10.1%
11076
 
3.1%
0804
 
2.3%
8780
 
2.3%
Other values (21)6199
18.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)34273
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o3807
11.1%
3705
10.8%
s3697
10.8%
r3697
10.8%
e3542
10.3%
c3503
10.2%
t3463
10.1%
11076
 
3.1%
0804
 
2.3%
8780
 
2.3%
Other values (21)6199
18.1%

price
Real number (ℝ)

High correlation 

Distinct473
Distinct (%)12.9%
Missing17
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean2.5336639
Minimum0.07
Maximum31.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2026-01-07T11:44:05.121804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.07
5-th percentile0.37
Q10.95
median1.52
Q32.75
95-th percentile8.5
Maximum31.5
Range31.43
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation2.9806235
Coefficient of variation (CV)1.1764084
Kurtosis14.933373
Mean2.5336639
Median Absolute Deviation (MAD)0.72
Skewness3.2791705
Sum9273.21
Variance8.8841164
MonotonicityNot monotonic
2026-01-07T11:44:05.350024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.2580
 
2.2%
1.564
 
1.7%
1.264
 
1.7%
0.963
 
1.7%
1.162
 
1.7%
1.460
 
1.6%
1.357
 
1.6%
252
 
1.4%
0.9552
 
1.4%
1.648
 
1.3%
Other values (463)3058
83.2%
ValueCountFrequency (%)
0.071
 
< 0.1%
0.161
 
< 0.1%
0.171
 
< 0.1%
0.191
 
< 0.1%
0.28
0.2%
0.216
0.2%
0.228
0.2%
0.231
 
< 0.1%
0.246
0.2%
0.2511
0.3%
ValueCountFrequency (%)
31.51
 
< 0.1%
27.51
 
< 0.1%
262
0.1%
251
 
< 0.1%
241
 
< 0.1%
231
 
< 0.1%
221
 
< 0.1%
203
0.1%
19.52
0.1%
193
0.1%

price_per_sqft
Real number (ℝ)

High correlation 

Distinct2651
Distinct (%)72.4%
Missing17
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean13892.668
Minimum4
Maximum600000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2026-01-07T11:44:05.580937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4715.95
Q16817.25
median9020
Q313880.5
95-th percentile33333
Maximum600000
Range599996
Interquartile range (IQR)7063.25

Descriptive statistics

Standard deviation23210.067
Coefficient of variation (CV)1.6706702
Kurtosis186.92801
Mean13892.668
Median Absolute Deviation (MAD)2794
Skewness11.43719
Sum50847166
Variance5.3870722 × 108
MonotonicityNot monotonic
2026-01-07T11:44:05.824848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000027
 
0.7%
800019
 
0.5%
500017
 
0.5%
1250014
 
0.4%
666613
 
0.4%
1111113
 
0.4%
2222213
 
0.4%
750012
 
0.3%
833312
 
0.3%
600011
 
0.3%
Other values (2641)3509
95.4%
(Missing)17
 
0.5%
ValueCountFrequency (%)
41
< 0.1%
51
< 0.1%
71
< 0.1%
91
< 0.1%
531
< 0.1%
571
< 0.1%
582
0.1%
601
< 0.1%
611
< 0.1%
791
< 0.1%
ValueCountFrequency (%)
6000001
< 0.1%
4000001
< 0.1%
3157891
< 0.1%
3083331
< 0.1%
2909481
< 0.1%
2833331
< 0.1%
2666661
< 0.1%
2611941
< 0.1%
2453981
< 0.1%
2416661
< 0.1%

area
Real number (ℝ)

High correlation  Skewed 

Distinct1312
Distinct (%)35.8%
Missing17
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean2888.3311
Minimum50
Maximum875000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2026-01-07T11:44:06.272542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile518.85
Q11232.25
median1733
Q32300
95-th percentile4246.2
Maximum875000
Range874950
Interquartile range (IQR)1067.75

Descriptive statistics

Standard deviation23167.506
Coefficient of variation (CV)8.0210699
Kurtosis942.02903
Mean2888.3311
Median Absolute Deviation (MAD)533
Skewness29.730956
Sum10571292
Variance5.3673333 × 108
MonotonicityNot monotonic
2026-01-07T11:44:06.505339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
165054
 
1.5%
135048
 
1.3%
180047
 
1.3%
195043
 
1.2%
324043
 
1.2%
270039
 
1.1%
90038
 
1.0%
200033
 
0.9%
225025
 
0.7%
240023
 
0.6%
Other values (1302)3267
88.8%
ValueCountFrequency (%)
504
0.1%
551
 
< 0.1%
561
 
< 0.1%
571
 
< 0.1%
602
0.1%
611
 
< 0.1%
672
0.1%
701
 
< 0.1%
721
 
< 0.1%
761
 
< 0.1%
ValueCountFrequency (%)
8750001
< 0.1%
6428571
< 0.1%
6200001
< 0.1%
5666671
< 0.1%
2155171
< 0.1%
989781
< 0.1%
827811
< 0.1%
655172
0.1%
652611
< 0.1%
582281
< 0.1%
Distinct2355
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Memory size428.2 KiB
2026-01-07T11:44:07.350289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length124
Median length119
Mean length54.236062
Min length12

Characters and Unicode

Total characters199426
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1849 ?
Unique (%)50.3%

Sample

1st rowSuper Built up area 1081(100.43 sq.m.)Carpet area: 650 sq.ft. (60.39 sq.m.)
2nd rowCarpet area: 1103 (102.47 sq.m.)
3rd rowCarpet area: 58141 (5401.48 sq.m.)
4th rowBuilt Up area: 1000 (92.9 sq.m.)Carpet area: 585 sq.ft. (54.35 sq.m.)
5th rowSuper Built up area 1995(185.34 sq.m.)Built Up area: 1615 sq.ft. (150.04 sq.m.)Carpet area: 1476 sq.ft. (137.12 sq.m.)
ValueCountFrequency (%)
area5573
18.5%
sq.m3655
12.1%
up3020
 
10.0%
built2316
 
7.7%
super1875
 
6.2%
sq.ft1751
 
5.8%
sq.m.)carpet1185
 
3.9%
sq.m.)built702
 
2.3%
carpet683
 
2.3%
plot681
 
2.3%
Other values (2846)8700
28.9%
2026-01-07T11:44:08.381471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
26464
 
13.3%
.20389
 
10.2%
a13154
 
6.6%
r9456
 
4.7%
e9320
 
4.7%
19205
 
4.6%
s7567
 
3.8%
q7431
 
3.7%
t7324
 
3.7%
u6770
 
3.4%
Other values (25)82346
41.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)199426
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
26464
 
13.3%
.20389
 
10.2%
a13154
 
6.6%
r9456
 
4.7%
e9320
 
4.7%
19205
 
4.6%
s7567
 
3.8%
q7431
 
3.7%
t7324
 
3.7%
u6770
 
3.4%
Other values (25)82346
41.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)199426
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
26464
 
13.3%
.20389
 
10.2%
a13154
 
6.6%
r9456
 
4.7%
e9320
 
4.7%
19205
 
4.6%
s7567
 
3.8%
q7431
 
3.7%
t7324
 
3.7%
u6770
 
3.4%
Other values (25)82346
41.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)199426
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
26464
 
13.3%
.20389
 
10.2%
a13154
 
6.6%
r9456
 
4.7%
e9320
 
4.7%
19205
 
4.6%
s7567
 
3.8%
q7431
 
3.7%
t7324
 
3.7%
u6770
 
3.4%
Other values (25)82346
41.3%

bedRoom
Real number (ℝ)

High correlation 

Distinct19
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3600761
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2026-01-07T11:44:08.533907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8976289
Coefficient of variation (CV)0.56475771
Kurtosis18.212873
Mean3.3600761
Median Absolute Deviation (MAD)1
Skewness3.4851418
Sum12355
Variance3.6009954
MonotonicityNot monotonic
2026-01-07T11:44:08.699376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
31496
40.7%
2942
25.6%
4660
17.9%
5210
 
5.7%
1124
 
3.4%
674
 
2.0%
941
 
1.1%
830
 
0.8%
1228
 
0.8%
728
 
0.8%
Other values (9)44
 
1.2%
ValueCountFrequency (%)
1124
 
3.4%
2942
25.6%
31496
40.7%
4660
17.9%
5210
 
5.7%
674
 
2.0%
728
 
0.8%
830
 
0.8%
941
 
1.1%
1020
 
0.5%
ValueCountFrequency (%)
211
 
< 0.1%
201
 
< 0.1%
192
 
0.1%
182
 
0.1%
1612
0.3%
141
 
< 0.1%
134
 
0.1%
1228
0.8%
111
 
< 0.1%
1020
0.5%

bathroom
Real number (ℝ)

High correlation 

Distinct19
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4245309
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2026-01-07T11:44:08.912789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9480681
Coefficient of variation (CV)0.56885693
Kurtosis17.542297
Mean3.4245309
Median Absolute Deviation (MAD)1
Skewness3.2488298
Sum12592
Variance3.7949693
MonotonicityNot monotonic
2026-01-07T11:44:09.136774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
31077
29.3%
21047
28.5%
4820
22.3%
5294
 
8.0%
1156
 
4.2%
6117
 
3.2%
941
 
1.1%
740
 
1.1%
825
 
0.7%
1222
 
0.6%
Other values (9)38
 
1.0%
ValueCountFrequency (%)
1156
 
4.2%
21047
28.5%
31077
29.3%
4820
22.3%
5294
 
8.0%
6117
 
3.2%
740
 
1.1%
825
 
0.7%
941
 
1.1%
109
 
0.2%
ValueCountFrequency (%)
211
 
< 0.1%
203
 
0.1%
184
 
0.1%
173
 
0.1%
168
 
0.2%
142
 
0.1%
134
 
0.1%
1222
0.6%
114
 
0.1%
109
0.2%

balcony
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size238.1 KiB
3+
1172 
3
1074 
2
884 
1
365 
0
182 

Length

Max length2
Median length1
Mean length1.3187381
Min length1

Characters and Unicode

Total characters4849
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row1
5th row3+

Common Values

ValueCountFrequency (%)
3+1172
31.9%
31074
29.2%
2884
24.0%
1365
 
9.9%
0182
 
4.9%

Length

2026-01-07T11:44:09.359850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-07T11:44:09.514518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
32246
61.1%
2884
 
24.0%
1365
 
9.9%
0182
 
4.9%

Most occurring characters

ValueCountFrequency (%)
32246
46.3%
+1172
24.2%
2884
 
18.2%
1365
 
7.5%
0182
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)4849
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
32246
46.3%
+1172
24.2%
2884
 
18.2%
1365
 
7.5%
0182
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4849
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
32246
46.3%
+1172
24.2%
2884
 
18.2%
1365
 
7.5%
0182
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4849
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
32246
46.3%
+1172
24.2%
2884
 
18.2%
1365
 
7.5%
0182
 
3.8%

floorNum
Real number (ℝ)

Zeros 

Distinct43
Distinct (%)1.2%
Missing19
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean6.7982504
Minimum0
Maximum51
Zeros129
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2026-01-07T11:44:09.748168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median5
Q310
95-th percentile18
Maximum51
Range51
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.0124542
Coefficient of variation (CV)0.884412
Kurtosis4.5153928
Mean6.7982504
Median Absolute Deviation (MAD)3
Skewness1.6936988
Sum24868
Variance36.149606
MonotonicityNot monotonic
2026-01-07T11:44:10.013960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
3498
13.5%
2493
13.4%
1351
 
9.5%
4316
 
8.6%
8195
 
5.3%
6183
 
5.0%
10179
 
4.9%
7176
 
4.8%
5169
 
4.6%
9161
 
4.4%
Other values (33)937
25.5%
ValueCountFrequency (%)
0129
 
3.5%
1351
9.5%
2493
13.4%
3498
13.5%
4316
8.6%
5169
 
4.6%
6183
 
5.0%
7176
 
4.8%
8195
 
5.3%
9161
 
4.4%
ValueCountFrequency (%)
511
 
< 0.1%
451
 
< 0.1%
441
 
< 0.1%
432
0.1%
401
 
< 0.1%
392
0.1%
381
 
< 0.1%
352
0.1%
342
0.1%
334
0.1%

facing
Categorical

High correlation  Missing 

Distinct8
Distinct (%)0.3%
Missing1045
Missing (%)28.4%
Memory size250.0 KiB
North-East
623 
East
623 
North
387 
West
249 
South
231 
Other values (3)
519 

Length

Max length10
Median length5
Mean length6.8381459
Min length4

Characters and Unicode

Total characters17998
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNorth-West
2nd rowNorth-East
3rd rowNorth-East
4th rowEast
5th rowNorth-East

Common Values

ValueCountFrequency (%)
North-East623
16.9%
East623
16.9%
North387
 
10.5%
West249
 
6.8%
South231
 
6.3%
North-West193
 
5.2%
South-East173
 
4.7%
South-West153
 
4.2%
(Missing)1045
28.4%

Length

2026-01-07T11:44:10.264205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-07T11:44:10.459342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
north-east623
23.7%
east623
23.7%
north387
14.7%
west249
 
9.5%
south231
 
8.8%
north-west193
 
7.3%
south-east173
 
6.6%
south-west153
 
5.8%

Most occurring characters

ValueCountFrequency (%)
t3774
21.0%
s2014
11.2%
h1760
9.8%
o1760
9.8%
E1419
 
7.9%
a1419
 
7.9%
N1203
 
6.7%
r1203
 
6.7%
-1142
 
6.3%
W595
 
3.3%
Other values (3)1709
9.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)17998
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t3774
21.0%
s2014
11.2%
h1760
9.8%
o1760
9.8%
E1419
 
7.9%
a1419
 
7.9%
N1203
 
6.7%
r1203
 
6.7%
-1142
 
6.3%
W595
 
3.3%
Other values (3)1709
9.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)17998
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t3774
21.0%
s2014
11.2%
h1760
9.8%
o1760
9.8%
E1419
 
7.9%
a1419
 
7.9%
N1203
 
6.7%
r1203
 
6.7%
-1142
 
6.3%
W595
 
3.3%
Other values (3)1709
9.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)17998
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t3774
21.0%
s2014
11.2%
h1760
9.8%
o1760
9.8%
E1419
 
7.9%
a1419
 
7.9%
N1203
 
6.7%
r1203
 
6.7%
-1142
 
6.3%
W595
 
3.3%
Other values (3)1709
9.5%

agePossession
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size268.1 KiB
Undefined
3411 
Under Construction
 
266

Length

Max length18
Median length9
Mean length9.6510742
Min length9

Characters and Unicode

Total characters35487
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUndefined
2nd rowUndefined
3rd rowUnder Construction
4th rowUndefined
5th rowUndefined

Common Values

ValueCountFrequency (%)
Undefined3411
92.8%
Under Construction266
 
7.2%

Length

2026-01-07T11:44:10.699025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-07T11:44:10.829926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
undefined3411
86.5%
under266
 
6.7%
construction266
 
6.7%

Most occurring characters

ValueCountFrequency (%)
n7620
21.5%
d7088
20.0%
e7088
20.0%
U3677
10.4%
i3677
10.4%
f3411
9.6%
r532
 
1.5%
o532
 
1.5%
t532
 
1.5%
266
 
0.7%
Other values (4)1064
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)35487
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n7620
21.5%
d7088
20.0%
e7088
20.0%
U3677
10.4%
i3677
10.4%
f3411
9.6%
r532
 
1.5%
o532
 
1.5%
t532
 
1.5%
266
 
0.7%
Other values (4)1064
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)35487
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n7620
21.5%
d7088
20.0%
e7088
20.0%
U3677
10.4%
i3677
10.4%
f3411
9.6%
r532
 
1.5%
o532
 
1.5%
t532
 
1.5%
266
 
0.7%
Other values (4)1064
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)35487
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n7620
21.5%
d7088
20.0%
e7088
20.0%
U3677
10.4%
i3677
10.4%
f3411
9.6%
r532
 
1.5%
o532
 
1.5%
t532
 
1.5%
266
 
0.7%
Other values (4)1064
 
3.0%

super_built_up_area
Real number (ℝ)

High correlation  Missing 

Distinct593
Distinct (%)31.6%
Missing1802
Missing (%)49.0%
Infinite0
Infinite (%)0.0%
Mean1925.2376
Minimum89
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2026-01-07T11:44:11.026178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum89
5-th percentile767
Q11479.5
median1828
Q32215
95-th percentile3185
Maximum10000
Range9911
Interquartile range (IQR)735.5

Descriptive statistics

Standard deviation764.17218
Coefficient of variation (CV)0.39692356
Kurtosis10.349191
Mean1925.2376
Median Absolute Deviation (MAD)372
Skewness1.8364563
Sum3609820.5
Variance583959.12
MonotonicityNot monotonic
2026-01-07T11:44:11.291854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
195037
 
1.0%
165037
 
1.0%
157825
 
0.7%
200025
 
0.7%
164022
 
0.6%
215022
 
0.6%
240819
 
0.5%
190019
 
0.5%
193018
 
0.5%
135017
 
0.5%
Other values (583)1634
44.4%
(Missing)1802
49.0%
ValueCountFrequency (%)
891
< 0.1%
1451
< 0.1%
1611
< 0.1%
2151
< 0.1%
2161
< 0.1%
3251
< 0.1%
3401
< 0.1%
3521
< 0.1%
3801
< 0.1%
4061
< 0.1%
ValueCountFrequency (%)
100001
< 0.1%
69261
< 0.1%
60001
< 0.1%
58002
0.1%
55141
< 0.1%
53502
0.1%
52002
0.1%
48901
< 0.1%
48571
< 0.1%
48482
0.1%

built_up_area
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct644
Distinct (%)38.1%
Missing1987
Missing (%)54.0%
Infinite0
Infinite (%)0.0%
Mean2379.5858
Minimum2
Maximum737147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2026-01-07T11:44:11.555868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile240.45
Q11100
median1650
Q32400
95-th percentile4691
Maximum737147
Range737145
Interquartile range (IQR)1300

Descriptive statistics

Standard deviation17942.88
Coefficient of variation (CV)7.5403375
Kurtosis1667.8704
Mean2379.5858
Median Absolute Deviation (MAD)650
Skewness40.706572
Sum4021500
Variance3.2194695 × 108
MonotonicityNot monotonic
2026-01-07T11:44:11.830829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
180041
 
1.1%
324037
 
1.0%
190034
 
0.9%
135033
 
0.9%
270033
 
0.9%
90028
 
0.8%
160026
 
0.7%
200024
 
0.7%
130024
 
0.7%
170023
 
0.6%
Other values (634)1387
37.7%
(Missing)1987
54.0%
ValueCountFrequency (%)
21
 
< 0.1%
141
 
< 0.1%
301
 
< 0.1%
331
 
< 0.1%
503
0.1%
531
 
< 0.1%
551
 
< 0.1%
561
 
< 0.1%
571
 
< 0.1%
605
0.1%
ValueCountFrequency (%)
7371471
 
< 0.1%
135001
 
< 0.1%
112861
 
< 0.1%
95001
 
< 0.1%
90007
0.2%
87751
 
< 0.1%
82861
 
< 0.1%
8067.81
 
< 0.1%
80001
 
< 0.1%
75002
 
0.1%

carpet_area
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct733
Distinct (%)39.2%
Missing1805
Missing (%)49.1%
Infinite0
Infinite (%)0.0%
Mean2529.1795
Minimum15
Maximum607936
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2026-01-07T11:44:12.111631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile350
Q1843
median1300
Q31790
95-th percentile2950
Maximum607936
Range607921
Interquartile range (IQR)947

Descriptive statistics

Standard deviation22799.836
Coefficient of variation (CV)9.0147166
Kurtosis604.53764
Mean2529.1795
Median Absolute Deviation (MAD)472.5
Skewness24.333239
Sum4734624
Variance5.1983254 × 108
MonotonicityNot monotonic
2026-01-07T11:44:12.588567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
140042
 
1.1%
160035
 
1.0%
180035
 
1.0%
120031
 
0.8%
150029
 
0.8%
165028
 
0.8%
135027
 
0.7%
130023
 
0.6%
100022
 
0.6%
145022
 
0.6%
Other values (723)1578
42.9%
(Missing)1805
49.1%
ValueCountFrequency (%)
151
 
< 0.1%
331
 
< 0.1%
481
 
< 0.1%
501
 
< 0.1%
591
 
< 0.1%
601
 
< 0.1%
661
 
< 0.1%
721
 
< 0.1%
76.443
0.1%
77.311
 
< 0.1%
ValueCountFrequency (%)
6079361
< 0.1%
5692431
< 0.1%
5143961
< 0.1%
645291
< 0.1%
644121
< 0.1%
581411
< 0.1%
549171
< 0.1%
488111
< 0.1%
459661
< 0.1%
344011
< 0.1%

study room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size237.0 KiB
0
2972 
1
705 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3677
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02972
80.8%
1705
 
19.2%

Length

2026-01-07T11:44:12.845129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-07T11:44:12.984256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
02972
80.8%
1705
 
19.2%

Most occurring characters

ValueCountFrequency (%)
02972
80.8%
1705
 
19.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)3677
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02972
80.8%
1705
 
19.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3677
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02972
80.8%
1705
 
19.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3677
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02972
80.8%
1705
 
19.2%

servant room
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size237.0 KiB
0
2349 
1
1328 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3677
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
02349
63.9%
11328
36.1%

Length

2026-01-07T11:44:13.164192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-07T11:44:13.302267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
02349
63.9%
11328
36.1%

Most occurring characters

ValueCountFrequency (%)
02349
63.9%
11328
36.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)3677
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02349
63.9%
11328
36.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3677
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02349
63.9%
11328
36.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3677
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02349
63.9%
11328
36.1%

store room
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size237.0 KiB
0
3339 
1
338 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3677
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03339
90.8%
1338
 
9.2%

Length

2026-01-07T11:44:13.468377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-07T11:44:13.605179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
03339
90.8%
1338
 
9.2%

Most occurring characters

ValueCountFrequency (%)
03339
90.8%
1338
 
9.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)3677
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
03339
90.8%
1338
 
9.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3677
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
03339
90.8%
1338
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3677
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
03339
90.8%
1338
 
9.2%

pooja room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size237.0 KiB
0
3021 
1
656 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3677
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03021
82.2%
1656
 
17.8%

Length

2026-01-07T11:44:13.782480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-07T11:44:13.920887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
03021
82.2%
1656
 
17.8%

Most occurring characters

ValueCountFrequency (%)
03021
82.2%
1656
 
17.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)3677
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
03021
82.2%
1656
 
17.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3677
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
03021
82.2%
1656
 
17.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3677
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
03021
82.2%
1656
 
17.8%

others
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size237.0 KiB
0
3272 
1
405 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3677
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
03272
89.0%
1405
 
11.0%

Length

2026-01-07T11:44:14.094630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-07T11:44:14.233663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
03272
89.0%
1405
 
11.0%

Most occurring characters

ValueCountFrequency (%)
03272
89.0%
1405
 
11.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)3677
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
03272
89.0%
1405
 
11.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3677
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
03272
89.0%
1405
 
11.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3677
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
03272
89.0%
1405
 
11.0%

furnishing_type
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size237.0 KiB
1
2436 
2
1038 
0
 
203

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3677
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
12436
66.2%
21038
28.2%
0203
 
5.5%

Length

2026-01-07T11:44:14.409612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-07T11:44:14.546827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
12436
66.2%
21038
28.2%
0203
 
5.5%

Most occurring characters

ValueCountFrequency (%)
12436
66.2%
21038
28.2%
0203
 
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)3677
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
12436
66.2%
21038
28.2%
0203
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3677
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
12436
66.2%
21038
28.2%
0203
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3677
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
12436
66.2%
21038
28.2%
0203
 
5.5%

luxury_score
Real number (ℝ)

Zeros 

Distinct161
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.512918
Minimum0
Maximum174
Zeros462
Zeros (%)12.6%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2026-01-07T11:44:14.731923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q131
median59
Q3110
95-th percentile174
Maximum174
Range174
Interquartile range (IQR)79

Descriptive statistics

Standard deviation53.059082
Coefficient of variation (CV)0.74195102
Kurtosis-0.88020421
Mean71.512918
Median Absolute Deviation (MAD)38
Skewness0.4590463
Sum262953
Variance2815.2662
MonotonicityNot monotonic
2026-01-07T11:44:15.003541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0462
 
12.6%
49348
 
9.5%
174195
 
5.3%
4460
 
1.6%
16555
 
1.5%
3855
 
1.5%
7252
 
1.4%
6047
 
1.3%
4245
 
1.2%
3745
 
1.2%
Other values (151)2313
62.9%
ValueCountFrequency (%)
0462
12.6%
56
 
0.2%
66
 
0.2%
741
 
1.1%
830
 
0.8%
99
 
0.2%
126
 
0.2%
1310
 
0.3%
1412
 
0.3%
1543
 
1.2%
ValueCountFrequency (%)
174195
5.3%
1691
 
< 0.1%
1689
 
0.2%
16721
 
0.6%
16610
 
0.3%
16555
 
1.5%
1613
 
0.1%
16028
 
0.8%
15923
 
0.6%
15834
 
0.9%

Interactions

2026-01-07T11:43:57.877083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:38.428582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:40.485858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:42.659841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:44.807761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:47.309980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:49.518635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:51.486280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:53.718804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:55.809114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:58.077207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:38.811140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:40.690701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:42.850044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:45.021733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:47.538086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:49.704686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:51.848852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:53.916262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:56.000916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:58.288470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:39.003103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:40.892909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:43.041234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:45.266786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:47.777051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-07T11:43:52.110319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:54.126918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:56.213845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:58.481093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:39.168817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:41.086925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:43.227220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:45.494916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:47.994848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:50.105398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:52.311577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:54.330464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:56.430700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:58.691322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:39.364545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:41.338720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:43.427270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:45.742692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:48.222544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:50.322102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:52.541992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:54.558313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:56.636551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:59.077496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:39.563559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:41.555182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:43.641805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:45.995209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:48.438603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:50.517101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:52.748789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:54.781574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-07T11:43:59.264148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:39.741856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:41.782360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-07T11:43:46.229468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:48.647875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:50.686522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:52.933738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:55.021509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:57.054129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:59.455761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:39.907823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:42.013736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:44.051520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:46.466851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:48.839397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:50.872824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:53.134701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:55.188276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:57.250445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:59.666275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:40.106691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:42.226833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:44.269100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:46.756500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:49.076961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:51.073704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:53.319181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:55.406358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:57.426688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:59.874542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:40.287561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:42.437174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:44.616705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:47.044502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:49.289758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:51.273129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:53.514373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:55.596446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T11:43:57.656892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-01-07T11:44:15.236732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
agePossessionareabalconybathroombedRoombuilt_up_areacarpet_areafacingfloorNumfurnishing_typeluxury_scoreotherspooja roompriceprice_per_sqftproperty_typeservant roomstore roomstudy roomsuper_built_up_area
agePossession1.0000.0000.0960.1290.1150.0000.0000.0680.1360.1670.1240.0000.0830.0560.0000.1220.1250.0560.0000.103
area0.0001.0000.0110.6870.6240.8350.8010.0220.1160.0430.2590.0420.0370.7440.2070.0280.0150.0390.0180.948
balcony0.0960.0111.0000.2250.1760.0000.0260.0160.0790.1780.2230.0820.1970.1360.0330.2140.4410.1460.1830.306
bathroom0.1290.6870.2251.0000.8620.4650.5990.044-0.0050.1950.1790.0700.2860.7200.4110.4720.5200.2440.1760.819
bedRoom0.1150.6240.1760.8621.0000.3800.5690.032-0.1040.1660.0570.0790.2910.6810.4170.5950.3170.2230.1540.800
built_up_area0.0000.8350.0000.4650.3801.0000.9691.0000.0910.0900.2890.0000.0000.6050.1320.0000.0000.0000.0000.926
carpet_area0.0000.8010.0260.5990.5690.9691.0000.0000.1590.0000.2390.0160.0000.6130.1360.0000.0000.0000.0030.894
facing0.0680.0220.0160.0440.0321.0000.0001.0000.0000.0550.0650.0000.0290.0210.0000.0940.0360.0360.0000.000
floorNum0.1360.1160.079-0.005-0.1040.0910.1590.0001.0000.0260.2320.0330.1020.001-0.1260.4850.0840.1120.0780.152
furnishing_type0.1670.0430.1780.1950.1660.0900.0000.0550.0261.0000.2380.0640.2130.1740.0220.0850.2660.1560.1380.132
luxury_score0.1240.2590.2230.1790.0570.2890.2390.0650.2320.2381.0000.1760.1890.2150.0540.3290.3470.2280.1830.222
others0.0000.0420.0820.0700.0790.0000.0160.0000.0330.0640.1761.0000.0330.0340.0360.0260.0000.1060.0310.084
pooja room0.0830.0370.1970.2860.2910.0000.0000.0290.1020.2130.1890.0331.0000.3340.0430.2520.2520.3050.3130.157
price0.0560.7440.1360.7200.6810.6050.6130.0210.0010.1740.2150.0340.3341.0000.7440.5430.3690.3030.2440.772
price_per_sqft0.0000.2070.0330.4110.4170.1320.1360.000-0.1260.0220.0540.0360.0430.7441.0000.2010.0440.0000.0300.287
property_type0.1220.0280.2140.4720.5950.0000.0000.0940.4850.0850.3290.0260.2520.5430.2011.0000.0650.2410.1281.000
servant room0.1250.0150.4410.5200.3170.0000.0000.0360.0840.2660.3470.0000.2520.3690.0440.0651.0000.1610.1850.584
store room0.0560.0390.1460.2440.2230.0000.0000.0360.1120.1560.2280.1060.3050.3030.0000.2410.1611.0000.2260.046
study room0.0000.0180.1830.1760.1540.0000.0030.0000.0780.1380.1830.0310.3130.2440.0300.1280.1850.2261.0000.121
super_built_up_area0.1030.9480.3060.8190.8000.9260.8940.0000.1520.1320.2220.0840.1570.7720.2871.0000.5840.0460.1211.000

Missing values

2026-01-07T11:44:00.250778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-01-07T11:44:00.606066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2026-01-07T11:44:01.136179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

property_typesocietysectorpriceprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompooja roomothersfurnishing_typeluxury_score
0flatsignature global park 4sector 360.827585.01081.0Super Built up area 1081(100.43 sq.m.)Carpet area: 650 sq.ft. (60.39 sq.m.)3222.0NaNUndefined1081.0NaN650.00000018
1flatsmart world gemssector 890.958600.01105.0Carpet area: 1103 (102.47 sq.m.)2224.0NaNUndefinedNaNNaN1103.011000138
2flatpyramid elitesector 860.4679.058228.0Carpet area: 58141 (5401.48 sq.m.)2210.0NaNUnder ConstructionNaNNaN58141.000000115
3flatbreez global hill viewsohna road0.325470.0585.0Built Up area: 1000 (92.9 sq.m.)Carpet area: 585 sq.ft. (54.35 sq.m.)22117.0NaNUndefinedNaN1000.00585.000000149
4flatbestech park view sanskrutisector 921.608020.01995.0Super Built up area 1995(185.34 sq.m.)Built Up area: 1615 sq.ft. (150.04 sq.m.)Carpet area: 1476 sq.ft. (137.12 sq.m.)343+10.0North-WestUndefined1995.01615.001476.0010012174
5flatsuncity avenuesector 1020.489022.0532.0Super Built up area 632(58.71 sq.m.)Carpet area: 532 sq.ft. (49.42 sq.m.)2215.0North-EastUndefined632.0NaN532.0001001159
6flatparas quartiergwal pahari7.5014018.05350.0Super Built up area 5350(497.03 sq.m.)443+20.0North-EastUndefined5350.0NaNNaN01011249
7flatexperion the heartsongsector 1082.008554.02338.0Super Built up area 2338(217.21 sq.m.)333+14.0EastUndefined2338.0NaNNaN01000195
8flatadani m2k oyster grandesector 1021.909105.02087.0Super Built up area 1889(175.49 sq.m.)3438.0North-EastUndefined1889.0NaNNaN010001165
9houseindependentsector 1051.2010122.01186.0Plot area 1185.51(110.14 sq.m.)6212.0North-WestUndefinedNaN1185.51NaN0000019
property_typesocietysectorpriceprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompooja roomothersfurnishing_typeluxury_score
3793flatgls arawali homessohna road0.274687.0576.0Carpet area: 576 (53.51 sq.m.)2221.0EastUndefinedNaNNaN576.000000135
3794houseindependentsector 278.0026298.03042.0Plot area 338(282.61 sq.m.)9934.0North-EastUndefinedNaN3042.0NaN111100110
3795flateldeco accoladesohna road0.875965.01459.0Super Built up area 1457(135.36 sq.m.)Carpet area: 849 sq.ft. (78.87 sq.m.)223+10.0NaNUndefined1457.0NaN849.010000172
3796flatparas dewssector 1060.926642.01385.0Super Built up area 1385(128.67 sq.m.)Built Up area: 940 sq.ft. (87.33 sq.m.)Carpet area: 845 sq.ft. (78.5 sq.m.)223+2.0EastUndefined1385.0940.0845.0000001174
3797housesurendra homes dayaindependentd colonysector 60.7515625.0480.0Built Up area: 480 (44.59 sq.m.)4421.0NaNUndefinedNaN480.0NaN0000010
3798flatpivotal devaansector 840.376346.0583.0Super Built up area 583(54.16 sq.m.)Carpet area: 483 sq.ft. (44.87 sq.m.)2215.0North-WestUndefined583.0NaN483.000000173
3799houseinternational city by sobha phase 1sector 1096.009634.06228.0Plot area 692(578.6 sq.m.)553+2.0South-WestUndefinedNaN6228.0NaN111101160
3800flatansal api celebrity suitessector 20.608163.0735.0Super Built up area 735(68.28 sq.m.)1115.0North-EastUndefined735.0NaNNaN00000267
3801houseindependentsector 4315.5028233.05490.0Plot area 610(510.04 sq.m.)5633.0EastUndefinedNaN5490.0NaN11110176
3802flatm3m ikonicsector 681.789128.01950.0Super Built up area 1950(181.16 sq.m.)Built Up area: 1845 sq.ft. (171.41 sq.m.)Carpet area: 1530 sq.ft. (142.14 sq.m.)333+27.0SouthUndefined1950.01845.01530.0000002126